Work

Dataset Analysis and Augmentation for Emoji-Sensitive Irony Detection

Year: 2019

Type: conference-paper

Abstract: Irony detection is an important task with applications in identification of online abuse and harassment.With the ubiquitous use of nonverbal cues such as emojis in social media, in this work we study the role of these structures in irony detection.Since the existing irony detection datasets have <10% ironic tweets with emoji, classifiers trained on them are insensitive to emojis.We propose an automated pipeline for creating a more balanced dataset.

Source: Workshop on Noisy User-generated Text (W-NUT)

DOI: 10.18653/v1/d19-5527

Authors: Shirley Anugrah Hayati, Aditi Chaudhary, Naoki Otani, Alan W Black

Institution: Carnegie Mellon University

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W2997860773

FWCI: 0.501

Cites: 23

Cited by: 7

Related to: 10